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The context of sustainable development dwells, quite significantly, upon the one of gender parity in a society and nation. This is so because the issue of gender equality is key to distributive justice, which is in turn much essential for creating a good amount of precondition for sustainable development. Academic inquests into the problem into the gender disparity are indicative of how gender disparity on economic and social parameter triggers a negative productivity change over time and space. Thus, the current chapter brings forth an analytical approach to contemplating into the above-mentioned narratives in both theoretical and empirical terms. The tack of our analysis is as follows. To begin with, this chapter develops an index to determine the extent of gender disparity in health, education and participation in workforce (namely, Gender Gap Index or GGI). Moving on, the study extends to looking at India changing dynamics on gender gap vis-à-vis developing and less-developed countries. Besides, a general equilibrium model has been developed to unfurl the fallout of gender disparity in terms of the wage gap between male and female workers in the labour force, extant and conditioned further by demographic and sociopolitical factors.

The aim of this study is to empirically explore and analyze the concrete tasks of output measurement and the inherent challenges related to these tasks in a traditional and autonomous professional public work setting – the judicial system.

The analysis of the tasks is based on a categorization of general performance measurement motives (control-motivate-learn) and main stakeholder levels (society-organization-professionals). The analysis is exploratory and conducted as an empirical content analysis on materials and reports produced in two performance improvement projects conducted in European justice organizations.

The identified main tasks in the different categories are related to managing resources, controlling performance deviations, and encouraging improvement and development of performance. Based on the results, key improvement areas connected to output measurement in professional public organizations are connected to the improvement of objectivity and fairness in budgeting and work allocation practices, improvement of output measures' versatility and informativeness to highlight motivational and learning purposes, improvement of professional self-management in setting output targets and producing outputs, as well as improvement of organizational learning from the output measurement.

The paper presents empirically founded practical examples of challenges and improvement opportunities related to the tasks of output measurement in professional public organization.

This paper fulfils an identified need to study how general performance management motives realize as concrete tasks of output measurement in justice organizations.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This article explores the scientific construction of library olfactory space, based on the case of the olfactory space in the Jiangsu University library. It specifically focuses on understanding the interaction between the physical architectural space of the library and users’ olfactory perception and behavioral activities, with the ultimate goal of creating a deeply integrated olfactory experience in the Jiangsu University Library.

In this article, an empirical research method was used to gather perceptions from 30 university student users regarding the library olfactory space and to understand their olfactory preferences and requirements for its construction. Through qualitative analysis of the interview texts, the study identified correlations between user perceptions and elements of the library olfactory space.

The qualitative analysis of user interview texts and results from the library olfactory space design experiment contributed to the design proposal for the Jiangsu University Library olfactory space. The design proposal for the Jiangsu University Library olfactory space is provided and includes library architecture, activity context, functional services, olfactory experience design and technological applications.

This case study takes the environment, development strategy and user needs of the Jiangsu University Library as its unique research background and as such is not universal or generalizable to other libraries.

This article differs from others by advocating for the innovative architectural spatial design of libraries through olfactory experience, breaking the traditional perception of libraries as solely through visual and auditory senses.

Benchmarking e-government and digital society developments using relevant indicators provides crucial insights into what aspects to consider while building a resilient digital society in which digital public services are delivered effectively and sustainably. The purpose of this paper is to analyse selected indices and indicators over the years and provide findings and recommendations on what indicators contribute most to the development.

A mixed research approach was used to conduct the research and collect, analyse and interpret data. A qualitative analysis involving the search, decomposition and comparison approaches to identify e-government and digital society reports, indices, rankings and indicators was followed by a quantitative analysis comprising of regression and cluster analyses.

The findings revealed that changes in the mix of indicators used by e-government and digital society indices can be attributed to advances in ICT and channels through which people communicate and receive information. The authors found that digital and telecommunication infrastructures and the quality of their parameters such as broadband have the biggest influence on progress of the e-government and digital societies developments and contribute most to clustering of the EU member states into groups.

The paper provides insights into how the structures of related indices changed over the years and how different indicators contribute to benchmarking of e-government and digital society developments by means of their weights. It provides governments with recommendations on which indicators to focus most.